surrey-nlp/Cyberbullying-Detection-CB1

Cyberbullying Detection — CB1

Dataset Description

CB1 is a multi-class text classification dataset for automated cyberbullying detection on social media. Each instance is a single social media post (sourced from Twitter/X) annotated with a cyberbullying category.

This dataset is part of the Cyberbullying-Detection collection on Hugging Face.

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Dataset Structure

Data Fields

Field	Type	Description
tweet_text	string	Raw social media post text
cyberbullying_type	string	Cyberbullying category (see label list below)

Label Classes

Label	Description
not_cyberbullying	Posts that contain no cyberbullying content. Includes general social commentary, opinions, and everyday conversation that may be critical or sarcastic but does not target individuals or groups with harmful intent.
gender	Posts that target individuals based on gender identity or sexual orientation. Includes harassment related to being male, female, non-binary, gay, lesbian, or transgender, as well as misogynistic and homophobic language.
religion	Posts that attack, mock, or dehumanise individuals or communities based on their religious beliefs or affiliation. Includes targeting of Christians, Muslims, Hindus, Jews, and other faith groups
ethnicity	Posts that demean or attack individuals based on their race or ethnic background. Includes the use of racial slurs and derogatory language directed at racial or ethnic minorities
age	Posts that bully or demean individuals based on their age. Includes harassment targeting young people (e.g. school-age bullying) as well as mockery of older individuals.
other_cyberbullying	Posts that constitute cyberbullying but do not fit neatly into the above categories. Includes general online harassment, trolling, and hostile behaviour not tied to a specific protected characteristic.

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Dataset Splits

The dataset is split as follows:

Split	Size	Description
train	75% of total	Training set
validation	2,000 rows	Development / validation set (sampled from the 25% held-out portion)
test	Remaining ~25% minus 2,000	Test set

Split Methodology

from sklearn.model_selection import train_test_split

# Step 1: 75% train, 25% test+dev
train_df, test_dev_df = train_test_split(df, test_size=0.25, random_state=42)

# Step 2: 2000 rows for dev, rest for test
dev_df = test_dev_df.sample(n=2000, random_state=42)
test_df = test_dev_df.drop(dev_df.index)

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Usage

from datasets import load_dataset

dataset = load_dataset("Washii/Cyberbullying-Detection-CB1")

# Access splits
train = dataset["train"]
validation = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])
# {'text': '...', 'label': 'ethnicity/race'}

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Source Data

The original data is sourced from "Cyberbullying Detection" dataset in Kaggle, containing tweets annotated for cyberbullying across multiple categories. The full raw file is CB1.csv.

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Citation

If you use this dataset, please cite the original source appropriately.

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Dataset Card Authors

Uploaded and curated by Washii.